دورية أكاديمية
A Gaussian-process approximation to a spatial SIR process using moment closures and emulators.
| العنوان: | A Gaussian-process approximation to a spatial SIR process using moment closures and emulators. |
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| المؤلفون: | Trostle P; Department of Statistics, North Carolina State University, Raleigh, NC, 27607, United States., Guinness J; Department of Statistics and Data Science, Cornell University, Ithaca, NY, 14853, United States., Reich BJ; Department of Statistics, North Carolina State University, Raleigh, NC, 27607, United States. |
| المصدر: | Biometrics [Biometrics] 2024 Jul 01; Vol. 80 (3). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Oxford University Press Country of Publication: England NLM ID: 0370625 Publication Model: Print Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: March 2024- : [Oxford] : Oxford University Press Original Publication: Alexandria Va : Biometric Society |
| مواضيع طبية MeSH: | Zika Virus Infection*/epidemiology , Zika Virus Infection*/transmission , Computer Simulation* , Stochastic Processes*, Humans ; Normal Distribution ; Epidemiological Models ; Models, Statistical ; Markov Chains |
| مستخلص: | The dynamics that govern disease spread are hard to model because infections are functions of both the underlying pathogen as well as human or animal behavior. This challenge is increased when modeling how diseases spread between different spatial locations. Many proposed spatial epidemiological models require trade-offs to fit, either by abstracting away theoretical spread dynamics, fitting a deterministic model, or by requiring large computational resources for many simulations. We propose an approach that approximates the complex spatial spread dynamics with a Gaussian process. We first propose a flexible spatial extension to the well-known SIR stochastic process, and then we derive a moment-closure approximation to this stochastic process. This moment-closure approximation yields ordinary differential equations for the evolution of the means and covariances of the susceptibles and infectious through time. Because these ODEs are a bottleneck to fitting our model by MCMC, we approximate them using a low-rank emulator. This approximation serves as the basis for our hierarchical model for noisy, underreported counts of new infections by spatial location and time. We demonstrate using our model to conduct inference on simulated infections from the underlying, true spatial SIR jump process. We then apply our method to model counts of new Zika infections in Brazil from late 2015 through early 2016. (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.) |
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| معلومات مُعتمدة: | DMS2152887 National Science Foundation; R01ES031651-01 United States NH NIH HHS |
| فهرسة مساهمة: | Keywords: SIR models; emulator models; moment-closure approximations; spatiotemporal epidemiology |
| تواريخ الأحداث: | Date Created: 20240722 Date Completed: 20240722 Latest Revision: 20240724 |
| رمز التحديث: | 20240725 |
| مُعرف محوري في PubMed: | PMC11261348 |
| DOI: | 10.1093/biomtc/ujae068 |
| PMID: | 39036985 |
| قاعدة البيانات: | MEDLINE |
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